Institutions must monitor consumer transactions for risk on a daily or real-time basis. They use AML transaction monitoring software do the same. The software can provide financial institutions with a “whole picture” analysis of a customer’s profile, risk levels, and predicted future activity, as well as generate reports and create alerts to suspicious activity, by combining this information with analysis of customers’ historical information and account profile. Cash deposits and withdrawals, wire transfers, and ACH activity are all examples of transactions that may be tracked.
Sanctions screening, blacklist screening, and client profiling are all elements that may be included in AML transaction monitoring solutions.
How does Transaction Monitoring work?
Identifying suspicious behaviour:
The AML transaction monitoring process involves scanning transactions manually or electronically based on numerous characteristics such as customer and beneficiary identities, volume, amount, country of origin, and destination. This assesses if the information matches the bank’s current understanding of the customer. The goal of AML transaction monitoring is to notify the bank of any odd business contacts or activity so that it may report money laundering and suspicious transactions.
Automating the process:
Banks should also have a sufficient transaction monitoring system in place, which implies that the system must be appropriate for the risk profile, size, complexity, and operations of the bank. Although some small banks use manual transaction scanning, most multinational institutions are required to have an automated transaction monitoring AML system in place to detect and report money laundering and odd activities in a more efficient and effective manner.
Increase effectiveness and efficiency:
The scope and nature of the AML transaction monitoring procedure are undeniably important.
The scope and form of AML transaction monitoring should undoubtedly be risk-based. While higher-risk scenarios may necessitate more monitoring, lower-risk situations may necessitate less monitoring.
Banks should evaluate client risk profiles, information obtained during the Customer Due Diligence (CDD) process, and any information supplied by law enforcement or other authorities when creating risk criteria to account for any ML/FT scheme.
Create audit trails and increase confidence:
Transaction-monitoring AML programmes should be able to provide important information for the board of directors and senior management, such as changes in client profiles. The system should also be able to provide a centralised view of information by customer or product, or across group entities. This is especially significant when a bank’s clients are supplied by different business units, because it allows the bank to account for all of the risks posed by those customers.
Reporting Suspicious Activity and Suspicious Activity Report (SAR)
Banks have procedures and processes for identifying, investigating, and reporting suspicious transactions. The AML transaction monitoring processes include the automated or manual monitoring systems that help identify unusual or potentially suspicious-transaction activities which are further investigated to determine whether customers’ transactions are suspicious and if they should be reported to higher authorities.
A Money Laundering Reporting Officer (MLRO) with sufficient expertise and other resources should be available with the bank in order to implement the necessary monitoring programs. Implementation of transaction monitoring AML systems should ensure that it is aligned with the bank’s risk assessment results. It should also be aligned with the criminal typologies related to the customers, products, services, and geographies addressed within the risk assessment and CRR results.
The Money Laundering Reporting Officer (MLRO) responsible for identifying, investigating, and reporting suspicious transactions should be well-trained on internal policies, procedures, and legal requirements. They should be provided with the necessary resources and guidance on how to recognise suspicious activities based on applicable criminal typologies and schemes. It is important that employees of the FIs are protected by law in case of the breach for disclosure of information while filing an STR.
The institutions should be prohibited by law from disclosing that an STR is filed with the FIU.
During the filing of multiple STRs on a customer, or if an STR is suspected of serious criminal activity, FIs must immediately take appropriate steps to mitigate the risk by:
(i) taking approval from an AML officer or another decision-maker within the AML compliance function to continue with the business relationship
(ii) putting the customer under enhanced monitoring and setting up lower thresholds
(iii) limiting the number of transactions of product/services for the customer in case the risk cannot be mitigated, the bank should then consider closing the customer’s account.
Transaction Monitoring with Tookitaki
A regulatory technology company focused on AML, Tookitaki has developed a Federated Learning-enabled AML information sharing framework, titled the Typology Repository Management (TRM). Tookitaki has created an ecosystem of AML Knowledge through the Typology Repository (Hub) while breaking down silos through the AML Detection Engine (Spokes). Insights from the Hub can be seamlessly ingested through the Spokes by financial institutions to identify and prevent financial crime.
Typology Repository is a fast-growing database of AML typologies or scenarios sourced from a network of AML experts globally, including financial institutions, law enforcement and regulators, and non-profit organizations. Typologies refer to patterns that are used to finance or launder money for illicit activities like drug trafficking, forced labour, forgery, terrorism, etc. They map varied customer activities that represent suspicious behaviour without using any Personally Identifiable Information (PII).
Tookitaki Typology Repository is pre-packaged with Typology Developer Studio that allows the creation of typologies holistically through a No-Code user interface. Once created and verified, typologies can be downloaded by user institutions. Tookitaki AML engine – AMLS uses a proprietary AML insights language to deconstruct the typologies ingested from
Typology Repository into risk indicators and then generate automated thresholds based on customer risk levels. Finally, an inbuilt simulation engine validates typologies while using a maker-checker process to deploy them seamlessly.
TRM enhances our machine learning-based transaction monitoring solution with superior detection capabilities. It is helping banks and fintech firms with financial crime identification and prevention by democratising AML insights through privacy-protected federated learning and precise detection through a hyper configurable machine learning approach.
For more information on our transaction monitoring solution and the ways in which it supercharges your transaction monitoring capabilities, speak to one of our experts today.
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